New Learning Theory #cck11

I have just started this course and am getting my head around this new learning theory...I have underlined the points from George Siemens' paper that most strike a cord.

According to this, I am pretty smart! -Jennie (#cck11)

"The pipe is more important than the content within the pipe. Our ability to learn what we need for tomorrow is more important than what we know today" George Siemens

Connectivism

Connectivism is the integration of principles explored by chaos, network, and complexity and self-organization theories. Learning is a process that occurs within nebulous environments of shifting core elements – not entirely under the control of the individual. Learning (defined as actionable knowledge) can reside outside of ourselves (within an organization or a database), is focused on connecting specialized information sets, and the connections that enable us to learn more are more important than our current state of knowing.

Principles of connectivism:

• Learning and knowledge rests in diversity of opinions.
• Learning is a process of connecting specialized nodes or information sources.
• Learning may reside in non-human appliances. 
• Capacity to know more is more critical than what is currently known
• Nurturing and maintaining connections is needed to facilitate continual learning.
• Ability to see connections between fields, ideas, and concepts is a core skill.
• Currency (accurate, up-to-date knowledge) is the intent of all connectivist learning activities.
• Decision-making is itself a learning process. Choosing what to learn and the meaning of incoming information is seen through the lens of a shifting reality. While there is a right answer now, it may be wrong tomorrow due to alterations in the information climate affecting the decision.
  
  
  
  
  
  
  
  
  The table below indicates how prominent learning theories differ from connectivism:
  
  

  Property	Behaviourism	Cognitivism	Constructivism	Connectivism
  How learning occurs	Black box—observable behaviour main focus	Structured, computational	Social, meaning created by each learner (personal)	Distributed within a network, social, technologically enhanced, recognizing and interpreting patterns
  Influencing factors	Nature of reward, punishment, stimuli	Existing schema, previous experiences	Engagement, participation, social, cultural	Diversity of network, strength of ties, context of occurrence
  Role of memory	Memory is the hardwiring of repeated experiences—where reward and punishment are most influential	Encoding, storage, retrieval	Prior knowledge remixed to current context	Adaptive patterns, representative of current state, existing in networks
  How transfer occurs	Stimulus, response	Duplicating knowledge constructs of “knower”	Socialization	Connecting to (adding) nodes and growing the network (social/conceptual/biological)
  Types of learning best explained	Task-based learning	Reasoning, clear objectives, problem solving	Social, vague (“ill defined”)	Complex learning, rapid changing core, diverse knowledge sources